Method and apparatus for determining a respiration parameter in a medical device

ABSTRACT

A method of determining a respiration parameter in a medical device in which pressure signals are sensed to generate corresponding sample points, and a breath detection threshold is continuously adjusted in response to the generated sample points to generate a current adjusted breath detection threshold. A current generated sample point is compared to the current adjusted breath detection threshold, and the continuous adjusting of the breath detection threshold is suspended and the breath detection threshold is equal to the most current adjusted breath detection threshold generated prior to the suspending in response to the comparing. A next sample point, generated subsequent to the suspending, is compared to the set breath detection threshold, and the respiration parameter is determined in response to the comparing of a next sample point to the set breath detection threshold.

RELATED APPLICATION

The present application claims priority and other benefits from U.S.Provisional Patent Application Ser. No. 61/098,282, filed Sep. 19, 2008,entitled “PRESSURE DERIVED RESPIRATION MONITORING”, incorporated hereinby reference in its entirety.

CROSS-REFERENCE TO RELATED APPLICATIONS

Cross-reference is hereby made to the commonly assigned related U.S.application Ser. Nos. 12/261,300, entitled “METHOD AND APPARATUS FORDETERMINING A RESPIRATION PARAMETER IN A MEDICAL DEVICE”, to Shrivastavet al.; 12/262,277, entitled “FILTERING OF A PHYSIOLOGIC SIGNAL IN AMEDICAL DEVICE”, to Cho et al.; 12/262,307, entitled “METHOD ANDAPPARATUS OF DETERMINING A RESPIRATION PARAMETER IN A MEDICAL DEVICE”,to Cho et al.; 12/262,320, entitled “METHOD AND APPARATUS FORDETERMINING RESPIRATORY EFFORT IN A MEDICAL DEVICE”, to Cho et al.,filed concurrently herewith and incorporated herein by reference intheir entireties.

TECHNICAL FIELD

The invention relates generally to medical devices and, in particular,to a medical device system and method for filtering of a physiologicsignal in a medical device.

BACKGROUND

Respiration monitoring is useful in diagnosing and managing pathologicalconditions. Respiratory rates can be measured and respiratory effort canbe observed during clinical office visits but potentially importantchanges that occur outside of the clinical setting cannot be observed.Heart failure patients can experience dyspnea (labored breathing) uponexertion. As heart failure worsens, dyspnea can occur at relatively lowlevels of exertion, at rest and during certain postures. Heart failurepatients can also experience disrupted breathing patterns such asCheyne-Stokes breathing and sleep apnea. Episodes of disrupted breathingpatterns are not easily captured during clinical office visits.Ambulatory monitoring of respiration is desirable for capturing usefuldiagnostic data and tracking a patient's disease state. Implantabledevices used for chronic monitoring of patients are generally minimizedin size to avoid patient discomfort. It is desirable to includerespiration monitoring capabilities in an implantable monitoring devicewithout substantially adding to the overall size and complexity of thedevice.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram of a patient monitoring system includingan implantable medical device coupled to a lead positioned within apatient's heart.

FIG. 2 is a functional block diagram of one embodiment of the IMD shownin FIG. 1.

FIG. 3 is a flow chart of one method for monitoring respiration.

FIG. 4 is a block diagram of one embodiment of a multi-stage filter usedfor deriving a respiration signal from an intracardiac pressure signal.

FIG. 5 is a plot of the amplitude frequency response for first, lowheart rate filter portion and the second, high heart rate filter portiondescribed in conjunction with FIG. 4.

FIG. 6 is a flow chart of one method for detecting breaths using arespiration signal derived from a right ventricular intracardiacpressure signal.

FIG. 7 is a plot of a pressure-derived respiration signal depicting thebehavior of an automatically adjusted threshold for detecting breathcycles.

FIG. 8 is a flow chart of one method for monitoring respiration using apressure-derived respiration signal.

FIG. 9 is a plot of a respiration signal illustrating a method forcomputing a respiratory effort.

DETAILED DESCRIPTION

In the following description, references are made to illustrativeembodiments. It is understood that other embodiments may be utilizedwithout departing from the scope of the invention. For purposes ofclarity, the same reference numbers are used in the drawings to identifysimilar elements. As used herein, the term “module” refers to anapplication specific integrated circuit (ASIC), an electronic circuit, aprocessor (shared, dedicated, or group) and memory that execute one ormore software or firmware programs, a combinational logic circuit, orother suitable components that provide the described functionality.

Various embodiments described herein utilize a pressure signal forderiving a respiration signal for respiration monitoring. As usedherein, the term “pressure signal” includes any pressure signal measuredwithin the body that includes a cardiac signal component and arespiration signal component. Such pressure signals include, forexample, a pressure signal measured within a cardiac chamber, alsoreferred to herein as an “intracardiac pressure signal”. Intracardiacpressure signals may be measured in the right or left atrium or in theright or left ventricle. Pressure signals used for deriving arespiration signal as described herein can include pressure signalsmeasured in any blood volume, including within a blood vessel. Pressuresignals used for deriving a respiration signal can also include internalpressure signals measured within a tissue or body cavity, such as in thepericardial space, mediastinal space, intrapleural space or within themyocardial tissue, all of which pressure signals may include both acardiac and respiratory component. It is noted that the relativecontributions of the respiratory and cardiac components to both theamplitude and the frequency content of the pressure signal will varydepending on the sensing site.

It is further noted that a respiration signal derived from an internalpressure signal is not a direct measure of the volume of air moved inand out of the lungs during breathing. Under many circumstances, theamplitude changes of the pressure-derived respiration signal willprovide a strong correlation to actual respiration volumes, i.e., theactual volume of air moving in and out of the lungs. This correlation,however, will depend on the airway resistance. For example, airwayresistance may increase in obstructive sleep apnea, causing a decreasein the inspired air volume. Yet at the same time, the peak-to-peakamplitude of a pressure-derived respiration signal will likely increase.This increase reflects an increased respiratory effort made by thepatient, i.e. increased work performed by the respiratory muscles toinhale, and does not correspond to an increase in the volume of inspiredair. As such, a pressure-derived respiration signal is a good indicatorof respiratory effort since the measured internal pressures will reflectthe effort being made by the patient to breath. Changes in thepressure-derived respiration signal may or may not be accompanied byactual changes in respired air volume, depending on the airwayproperties.

In summary, the term “respiration signal” as used herein, referring to asignal derived from a pressure signal, can be considered a “respiratoryeffort signal.” The negative-going signal peaks of the pressure-derivedrespiration signal are referred to herein as “peak inspiratory effort”since these peaks correspond to the patient's effort to inspire. Thepositive-going respiration signal peaks are referred to herein as “peakexpiratory effort” since these positive going peaks correspond to thepatient's effort to expire. The difference between a positive-going andnegative going peak of the pressure-derived respiration signal can bedetermined as one measure of respiratory effort as will be describedherein. However, it is recognized that the actual time point of maximuminspiratory effort and maximum expiratory effort as defined as actualwork performed by the muscles involved in respiration may or may notcoincide in time with the pressure-derived respiration signal peaks.

As will be described herein, the pressure-derived respiration signal, or“respiratory effort signal,” is useful for detecting temporal featuresof respiration, for example the timing of inspiration and expirationphases and the respiration rate. Detection of such temporal featuresallows patterns of abnormal breathing to be detected. Thepressure-derived respiration signal is also useful for determining ameasure of respiratory effort as described above. The pressure-derivedrespiration signal may or may not be mathematically correlated to actualrespired air volumes depending on individual circumstances.

The use of a pressure signal for deriving a respiration signal enablesrespiration monitoring to be incorporated in an implantable monitoringdevice that already includes a pressure sensor. For example, animplantable medical device that includes hemodynamic monitoring mayinclude a pressure sensor positioned in a heart chamber or blood vessel.Respiration monitoring using a respiration-derived pressure signal maybe incorporated in the hemodynamic monitoring device without requiringadditional sensors, leads, or circuitry.

An implantable hemodynamic monitor may include a pressure sensorpositioned along an intracardiac lead for measuring intracardiacpressure, for example right ventricular intracardiac pressure. Anintracardiac pressure signal includes a respiration component caused bychanges in intrathoracic pressure that occur during inspiration andexpiration. The respiration component of a pressure signal is typicallylower in frequency than the cardiac component. Methods and apparatusdescribed herein allow a respiration signal to be derived from theintracardiac pressure signal. Accurate breath detection and variousrespiration parameters can be determined from the derived respirationsignal.

FIG. 1 is a schematic diagram of a patient monitoring system includingan implantable medical device (IMD) 10 coupled to a lead 14 positionedwithin a heart 8 in a patient's body 6. IMD 10 is at least capable ofmonitoring physiological signals and may or may not include therapydelivery capabilities. IMD 10 may correspond to a variety of implantablemedical devices including a cardiac pacemaker, implantable cardioverterdefibrillator, implantable hemodynamic monitor, a drug pump, aneurostimulator or the like. Accordingly, IMD 10 may be coupled toadditional leads and/or catheters operatively positioned relative to thepatient's heart 8 or other body tissues for deployingstimulating/sensing electrodes, other physiological sensors, and/or drugdelivery ports. While lead 14 is shown terminated within the rightventricle of the patient's heart, it is recognized that lead 14 may beconfigured as a transvenous lead that extends into other heart chambersor blood vessels for positioning electrodes and/or physiological sensorsin a desired location.

In one embodiment, IMD 10 corresponds to an implantable hemodynamicmonitor capable of sensing and recording ECG signals and intracardiacpressure signals and storing cardiac electrical and hemodynamic data.ECG signals are sensed using one or more electrodes 18 carried by lead14 or using alternative electrodes (not shown) incorporated on thehermetically-sealed housing 12 of IMD 10. Housing 12 encloses circuitry(not shown) included in IMD 10 for controlling and performing devicefunctions and processing sensed signals.

Lead 14 is further provided with a pressure sensor 16. Pressure sensor16 is used for monitoring pressure within the right ventricle. Pressuresignals are monitored for determining metrics of hemodynamic functionuseful in monitoring heart failure status or diagnosing cardiacdysfunction. In embodiments described herein, the right ventricularintracardiac pressure signal obtained from sensor 16 is further used toderive a respiration signal. The respiration signal is processed formonitoring respiration and thereby provides additional useful datarelating to the patient's condition. While heart failure monitoring isone application in which respiration monitoring can be useful, it isrecognized that additional clinical applications will exist in whichrespiration monitoring using a pressure-derived respiration signal willbe beneficial, one example being sleep apnea or other respiratoryillnesses.

IMD 10 is capable of bidirectional communication with an externalprogrammer 26 via telemetry link 28. Programmer 26 is used to programthe operating mode and various operational parameters of IMD 10 as wellas interrogate IMD 10 to retrieve data stored by IMD 10. Stored data mayinclude data related to IMD function determined through automatedself-diagnostic tests as well as physiological data acquired by IMD 10using pressure sensor 16 and electrode(s) 18.

Programmer 26 is further shown in communication with a central database24 via communication link 30, which may be a wireless or hardwired link.Programming data and interrogation data may be transmitted via link 30.Central database 24 may be a centralized computer or a web-based orother networked database used by a clinician for remote monitoring andmanagement of patient 6. Various methods described herein and executedfor deriving a respiration signal from a pressure signal, detectingbreath cycles and deriving various respiration metrics may beimplemented in one or more of the IMD system components shown in FIG. 1,namely in the IMD 10, programmer 26 and/or central database 24, and mayinclude any combination of hardware, firmware and/or software.Programmer 26 may be embodied as a clinic-based programmer having fullIMD programming and interrogation functionality or a home-based monitorhaving interrogation and perhaps limited programming functionality andused for remote patient monitoring. It is recognized that other externaldevices, such as other physiological monitoring devices or other typesof programming devices, may be used in conjunction with IMD 10 andincorporate portions of the methods described herein.

FIG. 2 is a functional block diagram of one embodiment of IMD 10. IMD 10generally includes timing and control circuitry 52 and an operatingsystem that may employ microprocessor 54 or a digital state machine fortiming sensing and therapy delivery functions (when present) inaccordance with a programmed operating mode. Microprocessor 54 andassociated memory 56 are coupled to the various components of IMD 10 viaa data/address bus 55.

IMD 10 may include therapy delivery module 50 for delivering a therapyin response to determining a need for therapy, e.g., based on sensedphysiological signals. Therapy delivery module 50 may provide drugdelivery therapies or electrical stimulation therapies, such as cardiacpacing or anti-arrhythmia therapies. Therapies are delivered by module50 under the control of timing and control circuitry 52. IMD 10 can beimplemented as an interrupt-driven device in which case variouscomputations, algorithms, or other device functions are executed upongeneration of an interrupt signal.

Therapy delivery module 50 is typically coupled to two or more electrodeterminals 68 via an optional switch matrix 58. Switch matrix 58 may beused for selecting which electrodes and corresponding polarities areused for delivering electrical stimulation pulses. Terminals 68 may becoupled to connectors providing electrical connection to electrodesincorporated in IMD housing 12 or other lead-based electrodes, includingelectrode(s) 18 carried by lead 14 (shown in FIG. 1).

Electrode terminals 68 are also used for receiving cardiac electricalsignals through any unipolar or bipolar sensing configuration. Cardiacelectrical signals may be monitored for use in diagnosing or managing apatient condition or may be used for determining when a therapy isneeded and controlling the timing and delivery of the therapy. When usedfor sensing, electrode terminals 68 are coupled to signal processingcircuitry 60 via switch matrix 58. Signal processor 60 includes senseamplifiers and may include other signal conditioning circuitry and ananalog-to-digital converter. Electrical signals may then be used bymicroprocessor 54 for detecting physiological events, such as detectingand discriminating cardiac arrhythmias. As will be described herein,cardiac electrical signals received from terminals 68, which may beintracardiac EGM signals, far field EGM signals, or subcutaneous ECGsignals, are used in one embodiment for determining a heart rate. Theheart rate is used in performing heart rate dependent filtering of apressure signal for deriving a respiration signal.

IMD 10 is additionally coupled to one or more sensors of physiologicalsignals via sensor terminals 70. Physiological sensors include apressure sensor 16 as shown in FIG. 1 and may further includeaccelerometers, flow sensors, blood chemistry sensors, activity sensors,postures sensors, or other physiological sensors known for use withimplantable devices. Physiological sensors may be carried by leadsextending from IMD 10 or incorporated in or on the IMD housing 12.

Signals received at sensor terminals 70 are received by a sensorinterface 62 which provides sensor signals to signal processingcircuitry 60. Sensor interface 62 receives the sensor signal and mayprovide initial amplification, filtering, rectification, or other signalconditioning. Sensor signals are used by microprocessor 54 for detectingphysiological events or conditions. In particular, signals from pressuresensor 16 are processed by signal processor 60 and/or microprocessor 54for deriving a respiration signal and determining respiration parameterstherefrom. A respiration monitoring algorithm may be stored in memory 56and executed by microprocessor 54 with input received from electrodeterminals 68 and sensor terminals 70 for detecting respiration events.In one embodiment, microprocessor 54 is configured to executesoftware-implemented filtering operations for deriving a respirationsignal from a sensed pressure signal and further perform processing ofthe derived signal for determining respiration metrics.

Microprocessor 54 is further configured to determine an adjustablethreshold for detecting the onset of breath cycles and process thederived respiration signal using the automatically adjusted thresholdfor determining a breath rate. Additional algorithms may be implementedfor determining other respiration parameters such as a respiratoryeffort metric or for detecting respiration-related events such as apnea,hyperpnea, hyopopnea, Cheyne-Stokes breathing, or other abnormalbreathing patterns. The algorithms for executing the respiration signalderivation, breath rate determination, and other respiration parametercomputations may be stored in memory 56 and retrieved therefrom bymicroprocessor 54 as needed.

In alternative embodiments, filtering operations for deriving arespiration signal as well as breath rate and other respirationparameter determination may be implemented using dedicated hardwareand/or firmware implemented in signal processor 60. Signal processor 60may include a filter for receiving the pressure signal from sensorinterface 62. Signal processor 60 may be configured to digitize theinput signal and filter the signal using a hardware-implementedheart-rate dependent filter for deriving a respiration signal. Thesignal processor may thus receive a heart rate input signal on bus 55for filtering the pressure signal according to a determined heart rate.The heart rate is determined by microprocessor 54 using ECG/EGM signalsreceived from electrode terminals 68. Signal processor 60 may thenperform various threshold comparisons and peak detection operations aswill be described herein for detecting breaths, determining arespiratory effort, or computing other respiration parameters.

Respiration data may be stored for use in diagnosing or monitoring thepatient or for determining the need for delivering a therapy undercontrol of the operating system. The operating system includesassociated memory 56 for storing a variety of programmed-in operatingmodes and parameter values that are used by microprocessor 54. Thememory 56 may also be used for storing data compiled from sensedphysiological signals and/or relating to device operating history fortelemetry out on receipt of a retrieval or interrogation instruction.Microprocessor 54 may respond to the respiration data by altering atherapy, triggering data storage, enabling other sensors for acquiringphysiological data, or triggering alert 74 to generate an alert signalto the patient or a caregiver that a serious condition has been detectedthat may require medical intervention. Data relating to respiration maybe stored in memory 56 for later retrieval.

IMD 10 further includes telemetry circuitry 64 and antenna 65.Programming commands or data are transmitted during uplink or downlinktelemetry between IMD telemetry circuitry 64 and external telemetrycircuitry included in a programmer or monitoring unit as shown in FIG.1.

FIG. 3 is a flow chart of one embodiment of a method 100 for monitoringrespiration using a pressure-derived respiration signal. Flow chart 100and other flow charts presented herein are intended to illustrate thefunctional operation of a medical device system, and should not beconstrued as reflective of a specific form of software or hardwarenecessary to practice embodiments described herein. It is believed thatthe particular form of software, firmware and/or hardware will bedetermined primarily by the particular system architecture employed inthe device system and by the particular detection and therapy deliverymethodologies employed by the implantable device. Providing software,firmware and/or hardware to accomplish the operations described hereinin the context of any modern implantable device system, given thedisclosure herein, is within the abilities of one of skill in the art.

Methods described in conjunction with flow charts presented herein maybe implemented in a computer-readable medium that includes instructionsfor causing a programmable processor to carry out the methods described.A “computer-readable medium” includes but is not limited to any volatileor non-volatile media, such as a RAM, ROM, CD-ROM, NVRAM, EEPROM, flashmemory, and the like. The instructions may be implemented as one or moresoftware modules, which may be executed by themselves or in combinationwith other software.

At block 102 a pressure signal is sensed using an implantable sensor.The sensed signal will include a cardiac component and respirationcomponent and may include other noise and artifacts due to patientmovement, coughing, sneezing, etc. The implantable sensor may be placedin a blood volume, for example in a heart chamber for measuring anintracardiac pressure signal or, alternatively, in a blood vessel. Inone embodiment, the pressure sensor is positioned in the right ventriclefor right ventricular intracardiac pressure sensing. Alternatively, thesensor may be placed in any anatomic location exposed to fluctuations inintrathoracic pressures associated with breathing, including, but notlimited to, the pericardial space, mediastinal space, or intrapleuralspace. Pressure sensing at block 102 can be performed on a continuous orperiodic basis.

The sensed pressure signal is filtered at block 108 using a heart ratedependent filtering frequency response. A filter used at block 108 mayinclude a cascade of filters selected in various combinations to providedifferent filtering frequency responses, each corresponding to definedheart rate ranges. The heart rate dependent filter may alternativelyinclude a bank of individual filters each having a unique frequencyresponse selected individually for a particular heart rate range. Eachfilter provided for a given heart rate range may be a multistage filter,particularly when implemented in firmware or software. As describedabove, the heart rate dependent filter may be implemented in hardware,firmware or software. Thus, a heart rate dependent filter as describedherein generally refers to a composite filter including at least twodifferent single or multi-stage filter components or portions eachhaving a unique frequency response corresponding to separately definedheart rate ranges.

The filtering performed at block 108 provides a derived respirationsignal output as indicated at block 110, which may then be used as inputfor respiration monitoring algorithms at block 112. The respirationsignal output provided at block 110 may include a single signal obtainedby sequential operation of the different filter portions. In otherwords, the respiration signal is provided as a single continuous signalproduced by merging the sequential output signals of the two differentfilter portions. The respiration signal output may alternatively includemultiple filter output signals provided by each of the different heartrate dependent filter portions.

Respiration monitoring algorithms performed using a pressure-derivedrespiration signal will be described in greater detail below.Respiration monitoring performed at block 112 may be used forautomatically controlling a therapy and/or recording respiration datafor diagnostic or patient management purposes. In general, respirationmonitoring at least includes detecting breath cycles to allowdetermination of a breath rate and may include determination of otherrespiration metrics.

At block 106, a heart rate is determined. The heart rate may be derivedfrom the cardiac component of the pressure signal, for example usingpeak detection or threshold crossing algorithms for detecting each heartcycle length from the cardiac signal component. The quality of thecardiac signal component for use in determining a heart rate may beimproved by performing high pass or band pass filtering of the pressuresignal first. Alternatively, an ECG or EGM signal may be sensed asindicated by block 104 and used for determining a heart rate. Forexample, an EGM signal may be sensed using an intracardiac electrode ora subcutaneous ECG signal may be sensed using subcutaneously positionedelectrodes for determining a heart rate. Heart rate determination fromcardiac electrical signals is generally performed using R-wave detectionalgorithms and measuring R-R intervals.

The heart rate determined at block 106 is provided as input to therespiration monitoring algorithm 112 in one embodiment. When respirationdata is computed from multiple heart rate dependent filter portions,respiration data is selected for storage in memory and data reportingpurposes based on the determined heart rate. The respiration dataselected is the data computed from a filter output signal obtained fromthe filter having a frequency response corresponding to the determinedheart rate. The data obtained using filters having a frequency responsedesigned for heart rate ranges not matching the determined heart ratemay be discarded or archived.

Alternatively, the determined heart rate may be provided as input atblock 108 for use in selecting which of the heart rate dependent filterportions is selected for filtering the pressure signal. The heart ratedependent filter portions may be selected one at a time according to thedetermined heart rate to generate a single respiration signal at block110.

FIG. 4 is a block diagram of one embodiment of a multi-stage filter 200used for deriving a respiration signal from a pressure signal. Apressure signal, for example a right ventricular intracardiac pressuresignal, is sampled at a desired sampling rate, which is suitable for thepurposes of monitoring pressure and deriving cardiac functionparameters. In the example shown in FIG. 4, the input signal 202 forfilter 200 is a buffered 64 Hz, 8 bit signal. It is recognized thatother pre-filter sampling rate and bit resolution signals may beutilized.

The composite filter 200 includes two heart rate-dependent multi-stagefilter portions which are realized by selecting two differentcombinations of cascaded filter stages. The heart rate-dependent firstfilter portion is provided for filtering a respiration signal from thepressure signal during low heart rates, providing a low heart rateoutput 228. The first multi-stage filter portion includes filter stages204, 208, 212, 216, 218, 220,222 and 224.

The second heart rate-dependent filter portion is provided for derivingthe respiration signal during high heart rates. The second filterportion is a computational subset of the low heart rate filter portionso that additional filter stages or computations are not required toimplement the high heart rate filter portion. The second filter portionexcludes filter stages 220 and 222 which were included in the low heartrate filter portion. The second filter portion thus includes filterstages 204, 208, 212, 216 and 218 and a final filtering stage 226, whichmay be equivalent to the final filtering stage 224 of the low heart ratefilter portion. The second filter portion provides a high heart rateoutput 230.

In other embodiments, a single filter may be employed that is suitablefor all heart rates or additional filter portions may be employed forfiner heart rate resolution. The number of heart rate dependent filterportions included in the composite filter based on heart rate is notlimited to two as shown in FIG. 4, i.e., one filter portion selectedfrom the cascade of individual filter stages for a “high” heart raterange and another filter portion selected from the cascade of individualfilter stages for a “low” heart rate range. The heart-rate dependentfilter portions can include as many multi-stage or single stage filtersas needed for a desired resolution of heart rate ranges, i.e., low,high, and one or more intermediate heart rate ranges.

Each portion of the multi-stage filter is designed to produce a desiredoverall filter frequency response in the time domain selectableaccording to heart rate. In one embodiment, a first filter is selectedas one combination of the cascade of filter stages when the heart rateis equal to or less than 50 beats per minute. A second filter isselected as a different combination of the cascade of filter stages whenthe heart rate is greater than 50 beats per minute. The frequencyresponse for each heart-rate dependent filter portion will be optimizedaccording to the amplitude and frequency content of the respiratory andcardiac components contributing to the particular pressure signal beingsensed.

In an illustrative embodiment, the first filtering stage 204 averagesthe most recent four signal samples, i.e. the x(i) through x(i−3) samplepoint amplitudes are summed, and the summation is divided by four:y(i)=(x(i)+x(i−1)+x(i−2)+x(i−3))/4

The second filtering stage 208 averages the most recent signal sample,x(i), with twice the previous signal sample, 2*x(i−1), and the nextprevious signal sample (i−2):y(i)=(x(i)+2*x(i−1)+x(i−2))/4

The third filtering stage 212 averages x(i), 3*x(i−1), 3*x(i−2) andx(i−3):y(i)=(x(i)+3*x(i−1)+3*x(i−2)+x(i−3))/8

Stages 218, 220 and 222 can be implemented the same as the first stage204, i.e., an average of the most recent four signal sample amplitudes.The final output stages 224 and 226 are implemented as:y(i)=(15/16)*y(i−1)+x(i)−x(i−1)

While filter stages 224 and 226 are shown as the final stages in filter200, it is recognized that filter stages 224 and 226 could alternativelybe implemented earlier as a single stage included in both the high andlow heart rate filters, for example prior to stage 216, to reduceredundancy.

The multi-stage filtering approach allows a computationally efficientsoftware implementation of the desired heart rate dependent filteringresponses. The particular embodiment shown is computationally efficientwhile allowing the resolution of the derived respiration signal to bemaximized using the least number of bits. The heart-rate dependentfiltering approach allows limited coefficient filters and lower samplingrates to be used, which reduces the computation required to implement aparticular frequency response. The filter 204 reduces higher frequenciesin the pressure signal, such that the signal may be decimated by 4. Thenext filter 208 then may operate on one-quarter of the samples toachieve further low-pass filtering that allows decimation by 2, and soon. The constants in the illustrative equations given above allowmultiplications and divisions to be performed by shifts and adds. Theparticular order of the stages of the heart-rate dependent filters maybe rearranged.

It is recognized that multiple heart rate-dependent filters may each bedesigned and implemented in hardware as a single stage filter having thefinal filtering response of the multistage filters implemented insoftware as shown in FIG. 4. It is further recognized that a hardwareimplemented filter may be implemented having a variable frequencyresponse selectable based on a heart rate input. The filter frequencyresponse characteristics are chosen based on the known relationshipsbetween heart rates and respiration rates.

In an interrupt-driven device, the heart rate dependent filters and abreath detection algorithm to be described herein are run upon everyinterrupt signal, e.g. on an interrupt signal generated every onesecond. A limited number of samples from the previous interrupt intervalare saved to be used in the next interrupt interval to allow continuousfilter processing of signal samples. For example, the last 3 samplepoints of a previous interrupt interval may be saved to be used as thex(i−1), x(i−2) and x(i−3) sample points in the filtering equations shownabove with the first sample point x(i) taken from the current interruptinterval. In this way, continuous respiration monitoring can beperformed as long as the pressure signal is available.

Between filter stages 204 and 208, 208 and 212, and 212 and 216decimation is performed to reduce the sample number. The cascade offiltering and decimation shown in FIG. 4 allows the low heart rate andhigh heart rate filtering responses to be implemented within thecomputational bandwidth of a microprocessor included in an implantablemedical device. In the particular example shown, decimation by 4 occursat block 206. Decimation by 2 occurs at blocks 210 and 214. In the caseof a 64 Hz input signal, both the low heart rate filter output 228 andthe high heart rate filter output 230 is a 4 Hz signal.

Both of the low heart rate output 228 and the high heart rate output 230are provided as input to a common algorithm for detecting respirationevents. In other words, the respiration monitoring algorithm isimplemented to operate on both outputs 228 and 230, simultaneously orsequentially.

FIG. 5 is a plot 250 of the amplitude frequency response for the first,low heart rate filter portion and the second, high heart rate filterportion described in conjunction with FIG. 4. The high heart rate filterfrequency response 254 allows a higher frequency respiration signal tobe derived from the intracardiac pressure signal. The low heart ratefilter frequency response 252 reduces the likelihood of low frequency,cardiac-related pressure signals occurring at low heart rates frominterfering with the derived respiration signal. Using the heart rate asinput, the desired frequency response is selected and the pressuresignal is filtered accordingly. While a composite filter having twoselectable frequency responses for two different heart rate ranges isdescribed herein, it is recognized that a heart rate dependent filtermay include multiple filters or multiple selectable frequency responses,each of which may include multiple stages and selectable according tomultiple heart rate ranges.

FIG. 6 is a flow chart of one method 300 for detecting breaths using arespiration signal derived from a pressure signal, such as a rightventricular intracardiac pressure signal. Method 300 receives apressure-derived respiration signal input for use in detecting breaths.At block 301, the pressure-derived respiration signal output from a lowheart rate filter is provided as input to method 300. At block 302, thepressure-derived respiration signal output from a high heart rate filteris provided as input to method 300. Method 300 thus receives the signaloutput from each heart rate-dependent filter portion of a compositefilter. The respiration signals in block 301 and 302 are derived usingthe heart rate-dependent, multi-stage filtering described in conjunctionwith FIG. 4.

In one embodiment, the filters may be configured to operate in anon-simultaneous, i.e. sequential, manner selected according to apresently determined heart rate. In this case, method 300 receivessignal sample points from one heart rate-dependent filter portion at atime. The selected filter portion may be changing dynamically with heartrate changes, however a continuous digital signal will be received asinput for the breath detection method 300 allowing continuousrespiration signal monitoring. For example, upon an interrupt signal,the heart rate may be computed using the most recent cardiac sensedevent(s). The heart rate is then used for selecting which filter portionis operating to filter the pressure signal until the next heart rate isdetermined. In alternative embodiments, a running average heart rate isupdated upon each cardiac event, which may include sensed and pacedcardiac beats when cardiac pacing is present. The heart rate mayalternatively be an instantaneous heart rate based on a singleevent-to-event interval. The heart rate may be updated for each singleevent-to-event interval, such as each R-R interval, or every i^(th)event interval or other selected intervals. The heart rate mayalternatively be determined using several sensed and/or paced events andthe corresponding time period over which the events occur. It isrecognized that numerous methods for detecting an instantaneous oraveraged heart rate may be used.

The determined heart rate may be used for prospective selection of theheart rate dependent filter portion. A selected filter may continue tooperate for filtering the pressure signal until the next determinedheart rate. Alternatively, the selected filter portion may operate untilthe heart rate has been in a different heart rate range for apredetermined period of time or predetermined number of sensed/pacedevents, thereby introducing a hysteresis effect in the selection of thefilter portions.

In another embodiment, the rate-dependent filters operate in paralleleach providing a pressure-derived respiration signal as input to method300, to be processed simultaneously. In this case, breath cycles aredetected and a breath count is determined according to method 300 forboth the low HR respiration signal 301 and the high HR respirationsignal 302. A final breath count for a given interval of time is thenselected as the breath count determined from the filter output signalthat corresponds to a heart rate determined at the end of the giveninterval of time. In operation, the breath count determined for the highheart rate filter signal is the default breath count value to be storedfor data reporting purposes unless a heart rate below a threshold rate,for example 50 bpm, is detected, in which case the breath countdetermined using the low heart rate filter signal is stored for datareporting.

At block 304, a selected number of respiration signal sample points arecontinuously buffered in a first-in-first-out (FIFO) manner. If thefilter output signals 301 and 302 are provided simultaneously fordetermination of a breath count for both the low HR filter signal countand the high HR filter signal, buffered sample points are collected foreach signal in separate buffers for subsequent processing describedbelow. If the filter output signals provided as input at 301 and 302 areprovided sequentially according to a determined heart rate, bufferedsample points may be stored from the sequential input signals in asingle buffer for subsequent processing.

In one embodiment, ten sample points acquired from a 4 Hz filter outputsignal are stored in a memory buffer. Subsequent processing performed atblocks 306 through 324 is performed on both the low and high HR filtersignals when both signals are provided in parallel or on the single,sequentially combined LR and HR filter signals provided duringnon-simultaneous, sequential operation of the low and high HR filtersaccording to a determined heart rate.

The buffered sample points are used for continuously determining anautomatically adjusted breath detection threshold at block 306. Adynamically adjusted threshold for detecting breaths is determined basedon the varying amplitudes of the FIFO buffered sample points. Thethreshold is computed upon acquisition of each new sample point based onor as a function of the amplitudes of the buffered samples. In variousembodiments, the threshold may be computed as a threshold for detectinginspiration or a threshold for detecting expiration as the start of abreath cycle.

During inspiration, the intracardiac pressure signal is decreasing asintrathoracic pressure decreases. After high pass filtering, theinspiration phase of the filtered pressure signal can become negative inamplitude. The minimum peak of the pressure-derived respiration signalthus corresponds to the peak inspiratory effort. During expiration,intrathoracic pressure increases, and the respiration signal isgenerally positive in amplitude after highpass filtering of the pressuresignal occurs. The maximum peak of the pressure-derived respirationsignal corresponds to the peak expiratory effort.

A continuously adjusted breath-detection threshold may be based oneither of a minimum or a maximum amplitude, i.e., a minimum or a maximumvalue, of the buffered sample points. In one embodiment, a new breathcycle is detected during inspiration. The onset of the new breath cyclecorresponds to a sample point that is less than the automaticallyadjusted threshold. The auto-adjusted threshold is computed based on theminimum amplitude of the buffered sample points. The threshold may becomputed as a percentage, for example between 30% and 40% of the minimumamplitude. In a test comparison of breath rates determined from actualrespiration signals and from right ventricular pressure-derivedrespiration signals recorded in patients, an auto-adjusted thresholdcomputed as 37.5% of the minimum amplitude of the buffered sample pointswas found to result in a high accuracy of breath detection.

Generally, the minimum amplitude of the derived respiration signal is anegative value such that the threshold computed as a percentage of theminimum amplitude will be a negative value having an absolute value lessthan the minimum amplitude. It should be noted that, in unusualcircumstances, such as a patient on positive pressure ventilation, thepressure values may all be positive, but increasing and decreasingcyclically with the ventilator, with positive-going pressures associatedwith inspiration (forced by the ventilator) and negative-going pressuresassociated with expiration.

In some embodiments, a maximum and/or minimum limit may be set for theauto-adjusted threshold as indicated by input block 308. For example, amaximum threshold may be set which prevents the auto-adjusted thresholdfor detecting inspiration from ever exceeding the selected maximum. Whencomputing the threshold as a percentage of the minimum sample point, themaximum threshold may be 0 or a small negative value, for example (−0.5)or (−1.0) depending on the relative amplitudes of the sample points.

Upon detecting a negative-going threshold crossing at block 310, i.e. asample point having an amplitude that is less (more negative) than theauto-adjusted threshold, the continuous adjusting of the auto-adjustedthreshold is temporarily suspended and the auto-adjusted threshold islocked at its current value at block 312. The FIFO buffer continues toreceive new values but the threshold adjustment is suspended until apredetermined number of sample points (X) less than the current value ofthe auto-adjusted threshold are detected. In some embodiments, apredetermined number of consecutive sample points, e.g. 3 sample points,less than the threshold are required in order to confirm the detectionof inspiration. If this requirement is not met at decision block 314,the method 300 returns to block 306 to continue adjusting the thresholdand monitoring for the inspiration detection condition.

Once the inspiration detection requirement of X subthreshold samplepoints is satisfied at block 314, the automatic adjustment of thethreshold is resumed at block 316. As such, the temporary locking of thethreshold occurs for up to the predetermined number of consecutivesubthreshold sample points required for inspiration confirmation. Thethreshold is then adjusted based on the current content of the buffer atblock 316, which includes the subthreshold sample points used to confirminspiration detection. After confirming inspiration, method 300 monitorsfor expiration by comparing sample points to the auto-adjusted thresholdat block 318. An expiration threshold crossing is detected at block 318when a positive-going crossing occurs, i.e. a sample point amplitude isequal to or greater than the auto-adjusted threshold. The sample pointexceeding the threshold may be a positive value or a negative value.

In the embodiment illustrated by the flow chart of FIG. 6, theauto-adjusted threshold for detecting expiration continues to becomputed at block 316 in the same manner as the auto-adjusted thresholdfor detecting inspiration computed at block 306, i.e., a percentage ofthe minimum sample point amplitude stored in the FIFO buffer. Inalternative embodiments, the auto-adjusted threshold computed at block316, after confirming inspiration detection at block 314, is computeddifferently than the inspiration detection threshold. For example, theexpiration detection threshold may have a value that is based on apercentage of a maximum buffered sample point amplitude. In yet anotherembodiment, a percentage of the minimum buffered sample point amplitudeused to compute the expiration threshold may be different than thepercentage used to compute the inspiration threshold.

It is further recognized that computation of a single threshold ordistinct inspiration and expiration thresholds may involve more than asingle minimum or maximum buffered sample point. For example, an averageor other statistical aspect of two or more buffered sample points may beused in computing the auto-adjusted threshold. When computing anauto-adjusted threshold for expiration detection, a minimum thresholdvalue may be set to limit how low the threshold is set for detecting apositive-going threshold crossing.

In the example embodiment illustrated in FIG. 6, an expiration thresholdcrossing is detected at block 318 when a sample point that is greaterthan the auto-adjusted threshold is detected. The auto-adjustedthreshold is again locked at its current value at block 320 to allowmethod 300 to confirm expiration detection at block 322. Expirationdetection is confirmed by detecting a required number (Y) of consecutivesample points equaling or exceeding the locked threshold value (i.e.,suprathreshold sample points). If a subthreshold sample point isdetected before reaching the required number of consecutivesuprathreshold sample points, method 300 returns to block 316 tocontinue automatic adjustment of the threshold and monitoring forexpiration.

Once the required number of suprathreshold sample points is detected atblock 322, breath detection is confirmed as evidenced by the confirmeddetection of an expiration phase following an inspiration phase. Abreath counter is incremented at block 324. Incorporating a requirednumber of subthreshold sample points greater than one for confirming aninspiration threshold crossing (typically a negative-going crossing)helps to reduce the likelihood of false breath detections. Therequirement of one or more suprathreshold sample points before detectinga breath based only on the inspiration detection introduces a form ofhysteresis in the breath detection algorithm. This hysteresis reducesthe likelihood of inappropriate breath detections due to noise, such ascoughs, sneezes or sudden posture changes or other movement.

In FIG. 6, detection and confirmation of an inspiration phase of abreath cycle occurs at blocks 310 through 314 and is followed bydetection and confirmation of a subsequent expiration phase of thebreath cycle at blocks 316 through 322. Confirmation of each phaseoccurs before incrementing the breath count at block 324. Clearly theorder of detecting and confirming the inspiration and expiration phasescould be reversed from that shown in FIG. 6. In other words, blocks 318through block 322 could be performed for detecting and confirming theexpiration phase afterwhich blocks 310 through 314 are performed fordetecting and confirming the inspiration phase before incrementing thebreath count at block 324.

The practical number of required subthreshold and suprathreshold samplepoints for detecting a breath will be limited by the sampling rate.Requiring too many consecutive suprathreshold and subthreshold samplingpoints for confirming expiration and inspiration phases will limit thedetectable breath rate and could result in underestimation of the breathrate. Generally, two suprathreshold sample points preceding (orfollowing) an inspiration threshold crossing allows for accurate breathdetection using a 4 Hz sampling rate of a pressure-derived respirationsignal.

FIG. 7 is a plot 350 of a pressure-derived respiration signal 352depicting the behavior of an automatically adjusted threshold 354 fordetecting breath cycles. The auto-adjusted threshold 354, shown in heavyblack line varies with the respiration signal 352. Plateaus, for exampleat 358 and 360 occur when the auto-adjusted threshold 354 is locked forconfirming an inspiration phase or an expiration phase. Two occurrencesof the auto-adjusted threshold reaching the maximum threshold limit (0in this example) are seen at 356. Each breath is detected, as indicatedby “B”, upon a negative going threshold crossing that is preceded by arequired number of subthreshold sample points which are further precededby a required number of suprathreshold sample points. The number ofbreaths detected for a monitoring interval 362 are counted such that anumber of breaths per minute (18 in this example) can be stored indevice memory. The breath rate counted for repeating intervals 362 canbe stored in memory to provide continuous breath rate monitoring. Thisrespiration information can be utilized as an indicator of heart failurein the patient.

FIG. 8 is a flow chart of one method 500 for monitoring respirationusing a pressure-derived respiration signal. At block 502, a highresolution trend interval timer is started. At block 504, a lowresolution trend interval timer is started. The resolution trendintervals may be programmable by a physician. Respiration data may becollected and stored over one or more repeating time intervals toprovide varying resolutions of stored data. In one embodiment, a highresolution trend interval is approximately two seconds such that atleast a respiration rate, i.e., the number of breaths detected in a twosecond interval, and optionally other respiration parameters, is storedat the end of every two second interval. Such data storage may occurperiodically or continuously, twenty-four hours per day. A lowresolution trend interval may be on the order of several minutes, hoursor one day. In one embodiment, a low resolution trend interval is 8minutes. The number of breaths per minute in a low resolution trendinterval may be determined from the cumulatively stored high resolutiontrend data.

At block 506, a breath cycle onset is detected, using the methoddescribed in conjunction with FIG. 6. Upon the first breath detection,an apnea timer is started at block 508. If the apnea timer expiresbefore the next breath detection, as indicated by block 510, apnea isdetected at block 512. An apnea timer may be set to any desired timeinterval for detecting apnea. In one embodiment, the apnea timer is setat 10 seconds. Upon detecting apnea at block 512, a record of the apneaepisode may be stored in memory. A therapy response may be provided oran alarm generated. After each subsequent breath detection, the apneatimer is restarted at block 508.

The breath count is increased by one at block 514 in response to thebreath detection. A respiratory effort is determined at block 520 forthe previously detected breath cycle, which has ended upon the currentbreath detection. One method for determining a respiratory effort isdescribed below in conjunction with FIG. 9.

If the high resolution trend interval has expired, as determined atblock 522, the breath rate and a metric of respiratory effort for thetrend interval are stored at block 524. The high resolution trendinterval is restarted at block 526. If breath counts have beendetermined for parallel, simultaneous heart rate dependent filtersignals, the breath counts are temporarily stored for each filtersignal. The heart rate is determined at the end of the high resolutiontrend interval at block 518. The heart rate may be determined based onR-R intervals measured during the high resolution trend interval or anyother preceding time interval. In a composite filter including two heartrate dependent filters, if the heart rate is less than a rate threshold,such as 55 beats per minute, for example, the breath count andrespiratory effort determined for a low HR filter signal are stored fordata reporting purposes. If the heart rate is greater than thethreshold, the breath count and the respiratory effort determined forthe high HR filter signal are stored for data reporting purposes. Breathcounts temporarily stored for the filter signal(s) that are not selectedto be stored for data reporting purposes maybe discarded or archived.Alternatively, breath counts for both filters may be stored by the IMDuntil a heart rate determination is made at a future time, either by theIMD or by an external programmer or computer, at which time theappropriate breath rate data is utilized for reporting purposes.

If the low resolution trend interval has not yet expired, as determinedat block 528, method 500 returns to block 506 to wait for the nextbreath detection. If the low resolution trend interval has expired, thelow resolution breath rate and effort is stored at block 530. The lowresolution breath rate and effort may be determined from the highresolution data cumulatively stored over the low resolution trendinterval. The low resolution breath rate may be determined from a sum ofthe stored high resolution breath counts or an average breath ratedetermined using each of the stored high resolution breath rates storedover the low resolution trend interval. Alternatively, separate breathcounters may be used for each trend interval to separately determine thehigh and low trend interval breath rates.

The breath rate may be stored and made available for display to aclinician in breaths per minute. Respiratory effort may be stored as anaverage of the effort computed for each breath cycle over the lowresolution trend interval or as an average of the averages for each highresolution trend interval. After updating the low resolution data,method 500 returns to block 504 to restart the low resolution trendinterval.

FIG. 9 is a plot of a respiration signal 400 illustrating methods forcomputing a respiratory effort. Long-term or continuous ambulatorymonitoring of a measure of respiratory effort may be useful in detectingand assessing a respiratory parameter in heart failure patients, such asdyspnea, for example. The respiration signal 400 represents apressure-derived respiration signal. An arbitrary auto-adjustingthreshold 401 is illustrated. The detection of three breath cycles A, Band C is shown at 412, 414 and 416. Each breath detection 412, 414, and416 occurs upon detecting the required number of subthreshold samplepoints preceded by a required number of suprathreshold sample points.Using breath cycle B detected at 414 as an example, a first subthresholddata point 420′ is detected after which the threshold 401 remains lockedto confirm the next two consecutive sample points 420″ and 420′″ arealso subthreshold sample points. Upon detecting the third consecutivesubthreshold sample point 420′″, breath B is detected at 414. In otherembodiments, a different number of subthreshold sample points may berequired. The onset of the breath detection is shown to correspond tothe last confirming subthreshold sample point in FIG. 9, however it isrecognized that for timing and data extraction purposes, the onset ofthe breath cycle may correspond to any one of the subthreshold samplepoints 420′, 420″ or 420′″, collectively referred to as 420.

Upon confirming the required number of subthreshold sample points 420for inspiration detection, automatic adjustment of the threshold 401resumes until a first suprathreshold sample point 422′ is detected. Thethreshold 401 is again locked to allow confirmation of a required numberof consecutive suprathreshold sample points 422″ and 422′″. Theconfirmation of the suprathreshold sample points, collectively 422, isrequired before detection of the next breath C at 416 can occur.

Breath C 416 is detected upon confirming the required number ofsubthreshold sample points following the required suprathreshold samplepoints 422. Upon detection of breath C at 416, the respiratory effort432 for the preceding breath B can be determined. In one embodiment,respiratory effort 432 is defined as the difference between the minimumamplitude 408 (peak inspiratory effort) and the maximum amplitude 404(peak expiratory effort) of a breath cycle. The minimum amplitude 408occurs during an inspiration phase of breath B. The minimum amplitude issearched for during a minimum peak search interval 424. Interval 424 isdefined as the interval of time between the last suprathreshold samplepoint 426 used to confirm the expiration phase of breath A and the lastsuprathreshold sample point 422′″ used to confirm the expiration phaseof breath B. Interval 424 is thus inclusive of the inspiration phase ofbreath B (which occurs between peak expiratory effort 402 of breath Aand peak inspiratory effort 408 of breath B). A minimum peak search isperformed over interval 424 to reliably detect minimum peak 408 as thepeak inspiratory effort of breath B detected at 414.

The maximum peak 404 of breath B is searched for during a maximum peaksearch interval 430. The search interval 430 extends from the lastsubthreshold sample point 420′″ used to detect the onset of breath B at414 to the last subthreshold sample point 428 used to detect the onsetof breath C at 416. Interval 430 is thus seen to include the expirationphase of breath B, which extends between the peak inspiratory effort 408to peak expiratory effort 404. A peak search performed over interval 430will reliably identify peak 404 as the peak expiratory effort for breathB. The difference between maximum 404 and minimum 408 is computed as therespiratory effort 432 associated with breath B. It should be noted thatthe above described concept applies to a subject breathing normally, andthat, in the case of a patient on a positive pressure ventilator, therespiratory effort measures will not have typical meaning, but rather,will be influenced by the ventilator settings.

Alternatively, the respiratory effort may be computed as the slopebetween maximum 404 and minimum 408. In other embodiments, a measure ofrespiratory effort may be computed as a slope between any selectedsample point during the inspiration phase of a detected breath and aselected sample point during the expiration phase of the same detectedbreath.

The intervals for searching for the selected sample points used forcomputing a respiratory effort, such as intervals 424 and 430 mayalternatively be defined using different sample points than those shownin FIG. 9. For example, an interval to search for a point during theinspiration phase for use in computing effort may be defined as thefirst subthreshold sample point 420′ and the first suprathreshold samplepoint 422′. The interval to search for a point during the expirationphase for use in computing effort may be defined as the firstsuprathreshold point 422′ and the subsequent first subthreshold point434.

Thus, a system and method for respiration monitoring have been presentedin the foregoing description with reference to specific embodiments. Itis appreciated that various modifications to the referenced embodimentsmay be made without departing from the scope of the invention as setforth in the following claims.

1. A method of determining a respiration parameter in a medical device,comprising: sensing a pressure signals to generate corresponding samplepoints; continuously adjusting a breath detection threshold in responseto the generated sample points to generate a current adjusted breathdetection threshold determined from the generated sample points;comparing a current generated sample point of the generated samplepoints to the current adjusted breath detection threshold; suspendingthe continuous adjusting of the breath detection threshold and settingthe breath detection threshold equal to the most current adjusted breathdetection threshold generated prior to the suspending; comparing a nextsample point, generated subsequent to the suspending, to the set breathdetection threshold; and determining the respiration parameter inresponse to the comparing of a next sample point to the set breathdetection threshold.
 2. The method of claim 1, wherein the continuouslyadjusting a breath detection threshold comprises: storing apredetermined number of generated sample points; setting the breathdetection threshold equal to one of a minimum amplitude and a maximumamplitude of the current stored sample points; and updating the storedpredetermined number of generated sample points to include asubsequently generated sample point.
 3. The method of claim 1, furthercomprising determining whether both an inspiration detection thresholdand an expiration detection threshold are sequentially detected inresponse to the comparing a next sample point.
 4. The method of claim 1,wherein the respiration parameter is a breath count.
 5. The method ofclaim 1, further comprising: confirming detection of an one of aninspiration and an expiration; resuming the continuous adjusting of thebreath detection threshold; comparing a current sample point, generatedsubsequent to the resuming, to the current adjusted breath detectionthreshold; suspending the continuous adjusting of the breath detectionthreshold and setting the breath detection threshold equal to the mostcurrent adjusted breath detection threshold generated prior to thesuspending; confirming detection of the other of an inspiration and anexpiration; and incrementing a breath count in response to theconfirming detection of the other of an inspiration and an expiration.6. The method of claim 1, further comprising: determining a heart rate;filtering the sensed pressure signals in response to the determinedheart rate to generate a heart-rate dependent frequency response; andderiving a filtered signal in response to the heart rate dependentfrequency response, wherein the sample points are generated in responseto the derived filtered signal.
 7. The method of claim 6, whereinfiltering the sensed pressure signals comprises filtering the sensedpressure signals using a first filter having a first frequency responsecorresponding to the determined heart rate being greater than a heartrate threshold and a second filter having a second frequency responsedifferent from the first frequency response, the second frequencyresponse corresponding to the determined heart rate being less than theheart rate threshold.
 8. The method of claim 7, wherein the first filterand the second filter are selected one at a time in a sequential mannerin response to the determined heart rate and the filtered signal isobtained as a continuous signal comprising sequential outputs of thefirst filter and the second filter.
 9. The method of claim 7, whereinfiltering the sensed pressure signals comprises selecting the firstfilter and the second filter simultaneously for parallel filtering ofthe pressure signals, and wherein the filtered signal comprises a firstsignal component corresponding to an output of the first filter and asecond signal component corresponding to an output of the second filter.10. The method of claim 7, wherein the first and second filters eachcomprise multiple filtering stages, the first filter is a subset of themultiple stages of the second filter, and further comprising decimatingthe pressure signal following at least one of the multiple stages.
 11. Amethod of determining a respiration parameter in a medical device,comprising: sensing a pressure signals to generate corresponding samplepoints; continuously adjusting a breath detection threshold in responseto a minimum amplitude of a predetermined number of the generated samplepoints to generate a first detection threshold determined from thegenerated sample points; determining whether a current generated samplepoint of the generated sample points is less than the first inspirationdetection threshold; suspending the continuous adjusting of the breathdetection threshold and setting the first respiration detectionthreshold equal to the most current adjusted breath detection thresholdgenerated prior to the suspending; determining whether a firstpredetermined number of next sample points, generated subsequent to thesuspending, is less than the first respiration detection threshold;resuming the continuous adjusting of the breath detection threshold tocontinuously adjust the breath detection threshold in response to amaximum amplitude of the generated sample points to generate a secondrespiration detection threshold; determining whether a current generatedsample point is greater than the second respiration detection threshold;suspending the continuous adjusting of the breath detection thresholdand setting the expiration detection threshold equal to the most currentadjusted breath detection threshold generated prior to the suspending;determining whether a second predetermined number of next sample points,generated subsequent to the suspending, is greater than the secondrespiration detection threshold; and determining the respirationparameter in response to the predetermined number of next sample pointsbeing greater than the second respiration detection threshold.
 12. Themethod of claim 11, wherein the first respiration detection threshold isgenerated using a first computation scheme and the second respirationdetection threshold is generated using a second computation schemedifferent from the first computation scheme.
 13. The method of claim 11,wherein the first predetermined number of next sample points is equal tothe second predetermined number of next sample points.
 14. The method ofclaim 11, wherein the respiration parameter is a breath count.
 15. Themethod of claim 11, further comprising: determining a heart rate;filtering the sensed pressure signals in response to the determinedheart rate to generate a heart-rate dependent frequency response; andderiving a filtered signal in response to the heart rate dependentfrequency response, wherein the sample points are generated in responseto the derived filtered signal.
 16. The method of claim 15, whereinfiltering the sensed pressure signals comprises filtering the sensedpressure signals using a first filter having a first frequency responsecorresponding to the determined heart rate being greater than a heartrate threshold and a second filter having a second frequency responsedifferent from the first frequency response, the second frequencyresponse corresponding to the determined heart rate being less than theheart rate threshold.
 17. The method of claim 16, wherein the firstfilter and the second filter are selected one at a time in a sequentialmanner in response to the determined heart rate and the filtered signalis obtained as a continuous signal comprising sequential outputs of thefirst filter and the second filter.
 18. The method of claim 16, whereinfiltering the sensed pressure signals comprises selecting the firstfilter and the second filter simultaneously for parallel filtering ofthe pressure signals, and wherein the filtered signal comprises a firstsignal component corresponding to an output of the first filter and asecond signal component corresponding to an output of the second filter.19. The method of claim 16, wherein the first and second filters eachcomprise multiple filtering stages, the first filter is a subset of themultiple stages of the second filter, and further comprising decimatingthe pressure signal following at least one of the multiple stages.